Job description

Job Description: Agentic AI Engineering - Lead Programmer Analyst (Exp: 5 Yrs to 7 Yrs)

Design and deliver LLM applications and agents that retrieve enterprise knowledge, use tools and complete business workflows reliably. Remain hands-on in architecture, implementation and debugging while guiding junior engineers. The role focuses on applications built with existing models; traditional predictive-model training is not a core requirement.

Required experience and technical skills:

5+ years of professional software or data engineering experience, including hands-on delivery of LLM applications and agentic AI workflows.
Strong Python and SQL; API development with FastAPI or Flask, validation with Pydantic, and asynchronous service integration.
Practical experience with LLM APIs, streaming, token limits, prompt design, schema validation and model selection.
Hands-on LangChain experience or equivalent frameworks; ability to choose between fixed workflows and agent decisions.
RAG development using LangChain or LlamaIndex and vector stores such as pgvector or Qdrant, with filters and retrieval evaluation.
Ability to implement tool calling, persistent state, context and memory management, approval flows and reliable error handling.
Experience with AI evaluation, observability, access controls and secure tool execution, plus Git, testing, containers and cloud deployment.

Key responsibilities:

Translate business workflows into clear agent responsibilities, tool interfaces, success criteria and approval boundaries.
Build LLM applications using effective prompts, structured outputs and context management; choose models based on quality, latency and cost.
Develop RAG pipelines covering document ingestion, chunking, embeddings, hybrid retrieval, reranking and source-grounded answers.
Implement tool-using workflows with state, memory, checkpoints, human approvals, retries and safeguards against repeated actions or endless loops.
Integrate business APIs and data sources; enforce permissions and protect against prompt injection and sensitive-data exposure.
Build evaluation datasets, regression checks and traces; debug failures and improve task completion, response time and operating cost.
Own implementation quality and delivery while coaching juniors through design, coding, testing and production troubleshooting.

Mentoring and technical ownership:

Demonstrated ability to groom and guide junior engineers through pairing, design discussions, code reviews and constructive feedback.
Define tasks and acceptance criteria, remove technical blockers and review individual progress toward independent delivery.
Build reusable agent components and practical documentation; explain design choices and delivery risks to stakeholders.

Preferred skills:

Exposure to CrewAI / LangGraph for agent orchestration and multi-agent workflows; MCP integration and multimodal applications.
Exposure to Langfuse, LangSmith or equivalent tracing and evaluation tools; prompt/version management and evaluation gates.
Hugging Face Transformers for open-model integration; caching and model routing. Frameworks listed are alternatives; mastery of every tool is not required.

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